**In this paper, we**** build a low-level "vision system" in
successive tiers. The image is transformed into a series or
ternary fields that collect coarse local in****formation, only storing whether the image
response to a local filter is lower than a high threshold,
or smaller tha****n a
low threshol****d, or
neither (first image). A Markov random field is then trained
to learn a join distribution for these layers, within a
finite field of view(second image). This model is then used
as a saliency detector, selecting image patches that are
atypical wi****th respect to the learned model.**

Texture
classification using windowed Fourier filters, R. Azencott and J. P. Wang
and L. Younes, Pattern Analysis and Machine Intelligence, IEEE
Transactions on 19 148--153 (1997)

**This paper proposes to represent
texture using the energy (sum of squares) of Gabor
transforms over small windows. When textures are modeled as
stationary Gaussian random fields, these features can be
interpreted as non-parametric estimators of the spectral
density. This leads to the definition of a distance between
textures based on symmetrized Kullback-Leibler distances,
between stationary GRFs, which take a very simple form in
terms of spectral densities.
**

Synchronous Random Fields provide **a
representation for random fields over discrete grids that
can be sampled from using massively parallel schemes that
update all variables at the same time. The following
papers introduce and study these models in the context of
Image Processing and Neural Networks.**

Synchronous
Boltzmann machines and curve identification tasks, R.
Azencott and A. Doutriaux and L. Younes, Network: Computation in
Neural Systems 4 461--480 (1993)

Synchronous
random fields and image restoration, L
Younes, Pattern Analysis and Machine Intelligence, IEEE
Transactions on 20 (4), 380-390

Synchronous Boltzmann machines can be
universal approximators, L.
Younes, Applied Mathematics Letters
9 109--113 (1996)

Representation of Gibbs fields with
Synchronous Random Fields., L. Younes, Markov Processes
and Related Fields, vol. 2, 285–316. (1996)

Synchronous
image restoration, L Younes, Computer
Vision—ECCV'94, 213-217 (1994)

Learning
algorithms for extended models of Boltzmann machines, L Younes, ICPR, 602-602, 1994

A three tiered approach for articulated object action modeling and recognition, Le Lu, Gregory D Hager, Laurent Younes, NIPS (2004)

Clutter
invariant ATR, D. Bitouk and M. I.
Miller and L. Younes, Pattern Analysis and Machine
Intelligence, IEEE Transactions on 27
817--821 (2005)

Asymptotic
performance analysis for object recognition in clutter, D. Bitouk and M. I. Miller and L. Younes,
Proceedings of SPIE 5094 101--108 (2003)

Empirically
generated metric spaces for ATR in clutter, D. Bitouk and M. Miller and L. Younes,
Signals, Systems and Computers, 2002. Conference Record of the
Thirty-Sixth Asilomar Conference on 2
1407--1410 (2002)